Taking full advantage of the diverse assemblage of data at hand to produce time series of abundance. A case study on Atlantic salmon populations of Brittany

• Published in: Canadian Journal of Fisheries and Aquatic Sciences
• Main Authors: Lebot, Clément, Arago, Marie-Andrée, Beaulaton, Laurent, Germis, Gaëlle, Nevoux, Marie, Rivot, Etienne, Prévost, Etienne
• Other Authors: Ecologie Comportementale et Biologie des Populations de Poissons (ECOBIOP), Université de Pau et des Pays de l'Adour (UPPA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Pôle OFB-INRAE- Agrocampus Ouest-UPPA pour la gestion des migrateurs amphihalins dans leur environnement, AGROCAMPUS OUEST-Université de Pau et des Pays de l'Adour (UPPA)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Office français de la biodiversité (OFB), Office français de la biodiversité (OFB), Bretagne Grands Migrateurs (BGM), Dynamique et durabilité des écosystèmes : de la source à l’océan (DECOD), Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2022
• Subjects: Bayesian Modelling; Wild Populations; Clear Trend; Environmental Covariates; Hierarchical Bayesian; Fishing Effort; Full Advantage; Atlantic Salmon; Data Collection; Time Series; Britanny > France; [SDV]Life Sciences [q-bio]; [STAT.AP]Statistics [stat]/Applications [stat.AP]; Atlantic salmon
• Online Access: https://institut-agro-rennes-angers.hal.science/hal-03401246; https://doi.org/10.1139/cjfas-2020-0368

International audience Estimation of abundance with wide spatio-temporal coverage is essential to the assessment and management of wild populations. But, in many cases, data available to estimate abundance time series have diverse forms, variable quality over space and time and they stem from multiple data collection procedures. We developed a Hierarchical Bayesian Modelling (HBM) approach that take full advantage of the diverse assemblage of data at hand to estimate homogeneous time series of abundances irrespective of the data collection procedure. We apply our approach to the estimation of adult abundances of 18 Atlantic salmon populations of Brittany (France) from 1987 to 2017 using catch statistics, environmental covariates and fishing effort. Additional data of total or partial abundance collected in 4 closely monitored populations are also integrated into the analysis. The HBM framework allows the transfer of information from the closely monitored populations to the others. Our results reveal no clear trend in the abundance of adult returns in Brittany over the period studied.